Adversarial decision making arises in many situations, including counterterrorism, corporate competition, and federal regulation. Military leaders, corporate executives, and consumer groups regularly make large investments in the context of intelligent opposition. Such choices typically entail high-consequence outcomes conditional on low-probability events, with solutions drawn from the fields of decision analysis and game theory. However, previous work in decision analysis has largely overlooked adversarial situations---instead, it has focused on the uncertainties associated with natural disasters and concomitant costs. Game theory on the other hand has addressed head-on the assumption that the adversaries anticipate and adapt to each other's actions. Yet, in doing so, classical game theory assumed that agents act rationally (i.e. are risk insensitive) and know each others' payoff structures. As a result, current game theoretic and decision theoretic techniques result in formulations which make untenable assumptions about how humans process information and cope with uncertainty. These issues form the basis for the agenda of this workshop.
We invite members of the broad Algorithmic Decision Theory community to actively participate in this workshop through presentation of a poster at the Poster Session planned for Thursday evening, September 30, or through attending and contributing to the session discussions. Topics of interest for the Poster session include, but are not limited to:
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